

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase			:				int   =     {
    "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase			:				Tuple   =     ["AlbertTokenizer"]

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase			:				Union[str, Any]   =     ["AlbertTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase			:				Union[str, Any]   =     [
		    "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "AlbertForMaskedLM",
		    "AlbertForMultipleChoice",
		    "AlbertForPreTraining",
		    "AlbertForQuestionAnswering",
		    "AlbertForSequenceClassification",
		    "AlbertForTokenClassification",
		    "AlbertModel",
		    "AlbertPreTrainedModel",
		    "load_tf_weights_in_albert",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase			:				str   =     [
		    "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFAlbertForMaskedLM",
		    "TFAlbertForMultipleChoice",
		    "TFAlbertForPreTraining",
		    "TFAlbertForQuestionAnswering",
		    "TFAlbertForSequenceClassification",
		    "TFAlbertForTokenClassification",
		    "TFAlbertMainLayer",
		    "TFAlbertModel",
		    "TFAlbertPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase			:				Union[str, Any]   =     [
		    "FlaxAlbertForMaskedLM",
		    "FlaxAlbertForMultipleChoice",
		    "FlaxAlbertForPreTraining",
		    "FlaxAlbertForQuestionAnswering",
		    "FlaxAlbertForSequenceClassification",
		    "FlaxAlbertForTokenClassification",
		    "FlaxAlbertModel",
		    "FlaxAlbertPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert import AlbertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert_fast import AlbertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_albert import (
				    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AlbertForMaskedLM,
				    AlbertForMultipleChoice,
				    AlbertForPreTraining,
				    AlbertForQuestionAnswering,
				    AlbertForSequenceClassification,
				    AlbertForTokenClassification,
				    AlbertModel,
				    AlbertPreTrainedModel,
				    load_tf_weights_in_albert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_albert import (
				    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAlbertForMaskedLM,
				    TFAlbertForMultipleChoice,
				    TFAlbertForPreTraining,
				    TFAlbertForQuestionAnswering,
				    TFAlbertForSequenceClassification,
				    TFAlbertForTokenClassification,
				    TFAlbertMainLayer,
				    TFAlbertModel,
				    TFAlbertPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_albert import (
				    FlaxAlbertForMaskedLM,
				    FlaxAlbertForMultipleChoice,
				    FlaxAlbertForPreTraining,
				    FlaxAlbertForQuestionAnswering,
				    FlaxAlbertForSequenceClassification,
				    FlaxAlbertForTokenClassification,
				    FlaxAlbertModel,
				    FlaxAlbertPreTrainedModel,
				)
else:
		import sys

		lowercase			:				str   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_ = "x"     ,				lowerCamelCase_ = 10**-10     ,				lowerCamelCase_ = 1     ,				):
							A      :					int      		=					symbols(lowerCamelCase_		)
							A      :					List[str]      		=					lambdify(lowerCamelCase_     ,				lowerCamelCase_		)
							A      :					Tuple      		=					lambdify(lowerCamelCase_     ,				diff(lowerCamelCase_     ,				lowerCamelCase_		)		)

							A      :					Optional[int]      		=					starting_point

							while True:
														if diff_function(lowerCamelCase_		) != 0:
																					A      :					Union[str, Any]      		=					prev_guess - multiplicity * func(lowerCamelCase_		) / diff_function(
																					    lowerCamelCase_		)
														else:
																					raise ZeroDivisionError('''Could not find root'''		) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess		) < precision:
																					return next_guess

														A      :					Tuple      		=					next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")

		# Find root of polynomial
		# Find fourth Root of 5
		print(F"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}")

		# Find value of e
		print(
		    "The root of log(y) - 1 = 0 is ",
		    F"{newton_raphson('log(y) - 1', 2, variable='y')}",
		)

		# Exponential Roots
		print(
		    "The root of exp(x) - 1 = 0 is",
		    F"{newton_raphson('exp(x) - 1', 10, precision=0.0_05)}",
		)

		# Find root of cos(x)
		print(F"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")






